use std::sync::Arc;
use tokio::sync::Mutex;
use rayon::prelude::*;
use anyhow::{Result, Context};

/// 数据处理管道，支持并行处理和GPU加速
pub struct DataPipeline {
    num_threads: usize,
}

impl DataPipeline {
    /// 创建新的数据处理管道
    pub fn new(num_threads: usize) -> Self {
        Self { num_threads }
    }
    
    /// 并行处理数据
    pub async fn process_parallel(
        &self,
        data: &[f64],
        batch_size: usize,
        use_gpu: bool,
    ) -> Result<Vec<f64>> {
        if use_gpu {
            self.process_gpu(data, batch_size).await
        } else {
            self.process_cpu(data, batch_size)
        }
    }
    
    /// CPU并行处理
    fn process_cpu(&self, data: &[f64], batch_size: usize) -> Result<Vec<f64>> {
        let chunks: Vec<&[f64]> = data.chunks(batch_size).collect();
        let processed: Vec<Vec<f64>> = chunks.par_iter()
            .map(|chunk| {
                chunk.iter()
                    .map(|&x| self.process_single(x))
                    .collect()
            })
            .collect();
            
        Ok(processed.into_iter().flatten().collect())
    }
    
    /// GPU处理（当前为模拟实现）
    async fn process_gpu(&self, data: &[f64], batch_size: usize) -> Result<Vec<f64>> {
        // 目前返回CPU处理结果
        self.process_cpu(data, batch_size)
    }
    
    /// 处理单个数据点
    fn process_single(&self, value: f64) -> f64 {
        // 示例处理逻辑
        if value.is_nan() {
            0.0
        } else {
            value
        }
    }
}

#[cfg(test)]
mod tests {
    use super::*;
    
    #[tokio::test]
    async fn test_data_pipeline() {
        let pipeline = DataPipeline::new(4);
        let data = vec![1.0, 2.0, 3.0, 4.0, 5.0];
        
        // 测试CPU处理
        let result = pipeline.process_parallel(&data, 2, false).await.unwrap();
        assert_eq!(result.len(), data.len());
        
        // 测试GPU处理
        let result = pipeline.process_parallel(&data, 2, true).await.unwrap();
        assert_eq!(result.len(), data.len());
    }
}